---
title: "2017 NYC Health Inspections"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(p8105.datasets)
library(lubridate)
```
Column {data-width=350}
-----------------------------------------------------------------------
```{r}
data("rest_inspec")
rest_inspec =
rest_inspec %>%
filter(grade %in% c("A", "B", "C"), boro != "Missing", inspection_date >= as.Date("2017-01-01")) %>%
mutate(boro = str_to_title(boro),
dba = str_to_title(dba))
view(rest_inspec)
```
### Chart A
```{r}
#Which Pizzarias (see points) have FRSA related bugs, and what grades are they getting?
box_ggplot =
rest_inspec %>%
filter(str_detect(violation_description, "FRSA")) %>%
# distinct(camis, .keep_all = TRUE) %>%
# #Businesses can be visited more than once in a year. Should only count once, otherwise you can have more "Fly sightings" than restaurants, if many have more than one occurence..
mutate(boro = fct_infreq(boro)) %>%
ggplot(aes(x = boro, fill = grade)) +
labs(
title = "Do flies care about letter grade? Filth fly sightings by Borough",
y = "Number of sightings",
x = "Borough") +
geom_bar()
ggplotly(box_ggplot)
```
### Chart B
```{r}
rest_inspec %>%
filter(score >= 0) %>%
mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
plot_ly(
y = ~score, color = ~boro, type = "box", text = ~text_label, alpha = 0.5) %>% layout(title = 'Health Score by borough (lower is better)')
#Average grade is an A, and businesses are retested until they approach an A, thus all boroughs have similar statistics.
```
### Chart C
```{r}
# rest_inspec %>%
# #filter(str_detect(violation_description, "Evidence of")) %>%
# filter(score >= 0) %>%
# mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
# plot_ly(
# x = ~cuisine_description, y = ~score, type = "scatter", mode = "markers",
# color = ~score, text = ~text_label, alpha = 0.5) %>% layout(title = 'Health Score by Type of cuisine')
```
```{r}
p <- rest_inspec %>%
# count(boro) %>%
# mutate(boro = fct_reorder(boro, n)) %>%
#mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
ggplot(aes(x=inspection_date, y=score, color = grade)) +
geom_line() +
xlab("") +
theme(axis.text.x=element_text(angle=60, hjust=1)) +
labs(
title = "Scores over time",
y = "Score",
x = "Month")
ggplotly(p)
```
```{r}
rest_inspec %>% mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
filter(str_detect(violation_description, "Evidence of")) %>%
filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
mutate(boro = fct_infreq(boro)) %>%
plot_ly(
x = ~cuisine_description, y = ~score, type = "scatter", mode = "markers",
color = ~grade, text = ~text_label, alpha = 0.5)
```
```{r}
# rest_inspec %>% mutate(text_label = str_c("Health Score: ", score, "\nGrade: ", grade, "\nBusiness:", dba)) %>%
# filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
# #mutate(boro = fct_infreq(boro)) %>%
# plot_ly(
# x = ~cuisine_description, y = ~score, type = "box", mode = "markers",
# color = ~grade, text = ~text_label, alpha = 0.5)
```